Unordered Point Clouds has gained more attention in the past few years to learn directly from the raw data. PointNet [1]} and PointNet++ [2]} were one of the top papers that initiated this approach by applying NN models on raw 3D point clouds directly without the need to transform into volumes. A few follow-up papers worked on capturing geometric details from the raw data. PointCNN [3]} introduced an x-conv layer instead of a multi-layer perceptron(MLP) to aggregate information from neighbourhoods. Dynamic Graph CNN [4]} proposed a grouping method to replace the one in PointNet++ by constructing a local neighborhood graph and applying convolution-like operations on the edges. Although this approach was a success in classifying indoor shapes, it was not extensively tested on outdoor scenes specifically in the autonomous driving context.
